2024/06/19 by Alejandro Villena-Rodríguez, Villena-Rodríguez, Alejandro, Gerardo Gómez +11
Engineering · #Advanced MIMO Systems Optimization #Advanced Wireless Communication Technologies #FOS: Computer and information sciences #Information Theory (cs.IT) #Wireless Body Area Networks
paper · pdf · doi:10.48550/arxiv.2406.13675
openalex publication_date 2024/06/19 · openalex created_date 2024/06/22 · openalex updated_date 2026/07/28
The uplink of 5G networks allows selecting the transmit waveform between cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) and discrete Fourier transform spread OFDM (DFT-S-OFDM), which is appealing for cell-edge users using high-frequency bands, since it shows a smaller peak-to-average power ratio, and allows a higher transmit power. Nevertheless, DFT-S-OFDM exhibits a higher block error rate (BLER) which complicates an optimal waveform selection. In this paper, we propose an intelligent waveform-switching mechanism based on deep reinforcement learning (DRL). In this proposal, a learning agent aims at maximizing a function built using available throughput percentiles in real networks. Said percentiles are weighted so as to improve the cell-edge users' service without dramatically reducing the cell average. Aggregated measurements of signal-to-noise ratio (SNR) and timing advance (TA), available in real networks, are used in the procedure. Results show that our proposed scheme greatly outperforms both metrics compared to classical approaches.